The development and psychometric properties of an educational development impact questionnaire
Bibliographic record
Abstract
The purpose of this study was to develop and provide psychometric evidence of the Educational Development Impact Questionnaire (EDIQ) for evaluating complex outcomes of multifaceted educational development work within centres of teaching and learning. Our study addresses the need for a user-friendly and psychometrically sound survey instrument adaptable to differing higher education contexts. Our outcomes framework, mapping the intended short- and medium-term outcomes of our centre’s educational development activities, provided the necessary framework on which to create survey items. Scores from 267 instructors from a research-intensive university provided the data for examining the EDIQ’s initial factor structure using exploratory factor analysis. We justify our use of two different cut-scores to generate two models of factor structures. Factors common to both models are instructor growth, scholarship of teaching and learning, technology integration, impact on students, and knowledge enhancement. We discuss how the models provide different opportunities for assessing short-term and medium-term outcomes over time and the usefulness of the EDIQ beyond the current study context.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.043 | 0.098 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".